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A Process-aware Language Cognitive Diagnosis (PLCD) framework that uses language-derived structures as cognitive priors and response records to calibrate student posterior states is proposed, suggesting that structured language priors calibrated by response records can improve cold-start robustness and cognitive ground...
This work introduces ENOUGH, a method that iteratively appends behavioral records or emits STOP to construct profiles with adaptive lengths, which consistently outperforms strong heuristic and retrieval-augmented baselines in both effectiveness and efficiency.
When labeled data are scarce, off-the-shelf diffusion models can augment training sets for few-shot medical image classification, but not all generated samples are equally useful for the downstream task. Existing approaches largely improve synthetic data by increasing realism, diversity, or domain adaptation, while ove...
SEER (Self-grounded Evidence for Entity-Relation Reasoning), a training-free inference-time evidence interface for frozen VLMs, is proposed and established as the principal intervention, with reciprocal consistency as a smaller protocol-specific refinement.
This work turns an evidence-risk audit of text-rich multimodal large language models into an evidence-risk audit that couples answer behavior with geometric token-origin provenance, interventions, and realized cost; transparent training-free selectors isolate controlled operating points.
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